Air valve control method, device and equipment and storage medium

Abstract

The invention provides an air valve control method and device, equipment and a storage medium, relates to the technical field of automatic control, and solves the problem of poor sludge drying effect caused by air valve control lagging in related technologies. And the opening degree of the air valve is associated with the temperature change, so that the control problem of the air valve is converted into online quadratic programming solution, rolling optimization with constraints is carried out, the optimal control increment under the constraint condition is determined by integrating the control effect and the control cost, and then the air valve is adjusted. And the temperature field of the whole mesh belt area is controlled to be uniformly distributed in the drying treatment, so that the sludge drying effect is effectively improved, uniform drying can be realized in the sludge drying treatment, excessive and invalid air valve action and excessive drying of the dried area are avoided, air supply according to needs is realized, and energy conservation is facilitated.

Description

Technical Field

[0001] This application relates to the field of automatic control technology, and in particular to a method, device, equipment and storage medium for controlling a damper. Background Technology

[0002] Sludge drying, an indispensable part of wastewater treatment, has received widespread attention in the environmental protection field in recent years. Mesh belt sludge dryers are currently widely used sludge drying equipment, but in actual use, they suffer from uneven spatial distribution and time lag. Specifically, the large area of ​​sludge spread on the mesh belt makes it difficult for a single or few air outlets on the dryer to effectively cover the entire width of the belt, resulting in uneven sludge drying. Furthermore, the sludge takes time to dry from feeding to discharging on the mesh belt, and the control of the air valves only becomes apparent after this time, easily leading to overshoot and oscillation in the valve control. Related technologies have addressed this by increasing the number of air valves or introducing temperature feedback control schemes, but these still cannot effectively solve the problems of uneven spatial distribution and time lag leading to poor sludge drying results. Summary of the Invention

[0003] This application provides a method, device, equipment, and storage medium for controlling an air valve, which solves the problem of poor sludge drying effect caused by the lag in air valve control in related technologies. This solution can predict the sludge temperature, calculate the adjustment amount of the air valve in advance, and apply control actions, which helps to improve the sludge drying effect.

[0004] In a first aspect, this application provides a method for controlling air valves, applied to a sludge drying equipment. The sludge drying equipment is equipped with several temperature sensors at the feed end, intermediate section, and discharge end, and also with multiple air valves. The method includes: Given the initial temperature values ​​collected by all temperature sensors and the opening degree of all air valves within the current control cycle, the first temperature change value after a preset duration is predicted by a preset pulse response matrix. The pulse response matrix is ​​used to record the temperature increment detected by all temperature sensors at a unit opening degree for each air valve within the preset duration. Based on the first temperature change value and the initial temperature, the predicted sludge temperature value after a preset time is determined in the mapping relationship between the temperature value corresponding to the pre-set convolution sum function and the air valve control increment. The convolution sum function is used to determine the predicted sludge temperature value based on the first temperature change value, the initial temperature and the air valve control increment. Based on the predicted sludge temperature and the target temperature, an objective function is constructed that is related to the predicted sludge temperature, the target temperature, and the valve control increment. Under the preset function constraints, the objective function is minimized to obtain the target control increment of the corresponding target air valve, and the opening degree of the target air valve is adjusted according to the target control increment.

[0005] Secondly, this application also provides an air valve control device applied to sludge drying equipment. The sludge drying equipment is equipped with several temperature sensors at the feed end, intermediate section, and discharge end, and also with multiple air valves. The device includes: The incremental prediction module is configured to predict the first temperature change value after a preset time by using a preset pulse response matrix, given the initial temperature values ​​collected by all temperature sensors and the opening degree of all air valves within the current control cycle. The pulse response matrix is ​​used to record the temperature increment detected by all temperature sensors at a unit opening degree for each air valve within the preset time. The temperature prediction module is configured to determine the predicted sludge temperature value after a preset time period based on the first temperature change value and the initial temperature, in the mapping relationship between the temperature value corresponding to the pre-set convolution sum function and the air valve control increment. The convolution sum function is used to determine the predicted sludge temperature value according to the first temperature change value, the initial temperature and the air valve control increment. The function configuration module is configured to construct an objective function that is associated with the predicted sludge temperature value, the target temperature value, and the air valve control increment, based on the predicted sludge temperature value and the target temperature value. The incremental determination module is configured to minimize the objective function under preset function constraints to obtain the target control increment of the corresponding target air valve, and adjust the opening of the target air valve according to the target control increment.

[0006] Thirdly, this application also provides a sludge drying device, which includes: One or more processors; A storage device is provided for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the damper control method of this application.

[0007] Fourthly, this application also provides a storage medium for storing computer-executable instructions, which, when executed by a processor, are used to execute the damper control method of this application.

[0008] This application proposes a predictive model to forecast future system temperature changes and correlates valve opening with temperature variations. This transforms the valve control problem into an online quadratic programming problem, enabling constrained rolling optimization. By considering both control effectiveness and cost, the optimal control increment under constraints is determined, allowing for valve adjustment. This, in turn, ensures uniform temperature distribution throughout the conveyor belt during the drying process, effectively improving sludge drying. This results in uniform drying, avoiding excessive and ineffective valve operation and over-drying of already dried areas. On-demand air supply is achieved, contributing to energy conservation. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the steps of a damper control method provided in an embodiment of this application.

[0010] Figure 2 This is a schematic diagram illustrating the steps for determining the target control increment according to an embodiment of this application.

[0011] Figure 3 This is a schematic diagram illustrating the steps for correcting predicted sludge temperature values ​​according to an embodiment of this application.

[0012] Figure 4 This is a schematic diagram of the structure of a damper control device provided in an embodiment of this application.

[0013] Figure 5 This is a schematic diagram of the structure of a sludge drying device provided in an embodiment of this application. Detailed Implementation

[0014] The embodiments of this application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of this application and are not intended to limit the scope of this application. Furthermore, it should be noted that, for ease of description, the accompanying drawings only show the parts related to the embodiments of this application, not all structures. Those skilled in the art, after reading this specification, should be able to conceive that any combination of technical features can constitute an optional implementation method, provided that the technical features do not contradict each other.

[0015] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship. In the description of this application, "multiple" means two or more, and "several" means one or more.

[0016] Sludge drying is an indispensable part of wastewater treatment and has received widespread attention in the environmental protection field in recent years. Mesh belt sludge dryers are currently widely used sludge drying equipment, but in actual use, they suffer from uneven spatial distribution and time lag.

[0017] In cases of uneven spatial distribution, the sludge spreads over a large area on the mesh belt. The single or limited number of air outlets on a mesh belt sludge dryer cannot effectively cover the entire width of the belt, leading to uneven sludge drying. Regarding time lag, the sludge takes time to travel from infeed to discharge on the mesh belt. The control of the air valves only becomes apparent after this time, easily causing overshoot and oscillation in the valve control. Furthermore, there are variable operating conditions. The initial moisture content, feed thickness, and composition of the sludge change during the drying process. A fixed-parameter control system struggles to adapt to these changes, impacting product quality and energy consumption.

[0018] Related technologies employ control schemes that increase the number of air valves or introduce temperature feedback, such as adjusting multiple air valves to control the temperature during the drying process, thereby achieving uniform sludge drying. Another example is using negative feedback adjustment of air valves based on temperature changes. However, these technologies still cannot effectively solve the problem of poor sludge drying results caused by uneven spatial distribution and time lag; that is, the control strategies of these technologies struggle to promptly regulate temperature changes, thus affecting the sludge drying effect.

[0019] In response, this application provides a method for controlling a damper. The method treats the sludge drying equipment as a dynamic system with distributed parameters and multiple inputs and multiple outputs. By predicting future temperature changes in the system and constructing a corresponding function, the optimal damper control command is determined by solving a constrained optimization problem, thereby achieving a forward-looking, smooth, and uniform control effect.

[0020] Figure 1This diagram illustrates the steps of a damper control method according to an embodiment of this application. The method is applied to a sludge drying equipment. The sludge drying equipment has several temperature sensors installed at the feed end, intermediate section, and discharge end, and also has multiple dampers. Optionally, the temperature sensors can be arranged in an array at the feed end, intermediate section, and discharge end of the sludge drying equipment. Specific steps include S110-S140.

[0021] Step S110: After acquiring the initial temperature values ​​collected by all temperature sensors and the opening degree of all air valves within the current control cycle, predict the first temperature change value after a preset time period using a preset pulse response matrix.

[0022] Within each control cycle, the current sensor data is determined, such as the initial temperature values ​​collected by all temperature sensors and the opening degrees of all dampers. It's conceivable that the damper opening degree can be obtained through the corresponding opening degree sensor. Then, based on the acquired sensor data, temperature changes are predicted. Specifically, prediction is performed using a set impulse response matrix, which records the temperature increment detected by all temperature sensors at a unit opening degree for each damper within a preset time period. It's understandable that for each damper, when its opening degree changes while the opening degrees of other dampers remain constant, the temperature values ​​detected by temperature sensors in different areas will differ. Therefore, each element in the impulse response matrix represents the temperature increment detected by the current temperature sensor at a unit opening degree for that damper within the preset time period.

[0023] Optionally, the impulse response matrix is ​​a three-dimensional matrix associated with the damper, temperature sensor, and preset duration. Elements in the impulse response matrix represent temperature increments. These temperature increments are obtained by sequentially applying a step signal to each damper to record the response data of all temperature sensors within the preset duration. This step signal is used to control the opening degree of the damper. For example, applying a step signal to damper j to increase its opening degree by 10%, if the temperature change of temperature sensor i within the preset duration is 2.5℃, then the corresponding temperature increment per unit opening degree is 0.25℃. That is, for every 1% increase in the opening degree of damper j, the temperature value detected by temperature sensor i after the preset duration increases by 0.25℃ compared to before the preset duration. This temperature increment is recorded by each element in the impulse response matrix. For example, if the impulse response matrix is ​​represented by matrix G, where an element G(i, j, τ) represents the change in the temperature sensor i caused by a unit opening degree change of the j-th damper after the preset duration τ. It should be noted that in some embodiments, τ can also be represented by the number of sampling periods corresponding to the preset duration.

[0024] Furthermore, after determining the opening degree of the air valve, the first temperature change value after a preset time is predicted by the preset pulse response matrix. That is, according to the change of the air valve opening degree, the temperature change of each temperature sensor corresponding to the air valve can be determined by the corresponding element in the pulse response matrix. It can be imagined that the determined first temperature change value includes the value of the temperature change detected by each temperature sensor under the corresponding air valve opening degree.

[0025] Step S120: Based on the first temperature change value and the initial temperature, determine the predicted sludge temperature value after a preset time period in the mapping relationship between the temperature value corresponding to the preset convolution and function and the control increment of the air valve.

[0026] The convolution sum function, used to predict the temperature value of future output sludge, determines the predicted sludge temperature value based on a first temperature change, an initial temperature, and an air valve control increment. Given the first temperature change and the initial temperature, the convolution sum function represents the mapping relationship between the temperature value and the air valve control increment. Furthermore, according to the convolution sum function, the functional relationship between the predicted sludge temperature value and the air valve control increment is determined to ascertain the predicted sludge temperature value after a preset time period. Optionally, taking a configuration with 9 dampers as an example, the convolution function is used to output the sum of the cumulative product of the damper control increment and the first temperature change value, and the zero-input response value, which is then used as the predicted temperature value. The corresponding calculation formula is as follows:

[0027] in, To predict sludge temperature, The response value is zero. The first temperature change value determined by the pulse matrix. This is the increment for air valve control. Specifically, the zero-input response value is the sum of the initial temperature value and the second temperature change value detected by the sludge drying equipment while maintaining the air valve opening at a consistent degree for a preset time. This second temperature change value can be determined based on historical data. For example, if the historical data records that the temperature rose from 83°C to 88°C after the air valve was kept at a certain opening for a preset time, then the corresponding second temperature change value at that opening is 5.

[0028] Moreover, G(i,j,τ) l) The transmission characteristics of the corresponding air valve and temperature sensor, that is, when the opening of the j-th air valve changes by 1%, after τ After l sampling periods (corresponding to the preset duration), the temperature detected by the i-th temperature sensor will change by how much, i.e., the first temperature change value mentioned above. ΔU(j, k+l) represents the temperature change of the j-th air valve after the l sampling periods. l The incremental control of the air valve, for example, air valve F2 inl If 2% of the actions were performed in step 0, then ΔU(2, k+0) = 2.

[0029] It can be inferred that in the mapping relationship recorded by the above convolution and function, the valve control increment is an independent variable to be determined, while the final predicted sludge temperature value is a dependent variable. That is, when the valve control increment changes, the corresponding predicted sludge temperature value will also change. Therefore, the predicted sludge temperature value determined after prediction is a function value related to the valve control increment.

[0030] Step S130: Based on the predicted sludge temperature value and the target temperature value, construct an objective function that relates the predicted sludge temperature value, the target temperature value, and the valve control increment.

[0031] The target temperature value is the desired temperature to be achieved. For example, in a uniform temperature field, the set target temperature is the same at all detected points; that is, the desired temperature is the same for all temperature sensors. After determining the predicted sludge temperature value, an objective function is constructed that relates the predicted sludge temperature value, the target temperature value, and the valve control increment. This objective function represents the influence of changes in the valve control increment on the predicted sludge temperature value tending towards the target temperature value. It can be understood that in the objective function, as the valve control increment increases, the predicted sludge temperature value also increases. Therefore, by increasing or decreasing the valve control increment, the predicted sludge temperature value can be made to tend towards or move away from the target temperature value. Based on this objective function, an optimization problem can be defined, and the valve control increment can be determined by minimizing the objective function.

[0032] Step S140: Under the preset function constraints, minimize the objective function to obtain the target control increment of the corresponding target air valve, and adjust the opening of the target air valve according to the target control increment.

[0033] It is conceivable that in the process of solving the objective function, the constraints based on the function constraints are used to constrain the parameters in the objective function, such as the opening degree of the damper. Optionally, the opening degree range of the damper can be constrained so that the opening degree of the damper varies within the set range, and / or the increment of the opening degree of the damper can be constrained to avoid the damper opening degree from changing too much.

[0034] Furthermore, the objective function is minimized under the constraint that the change in the damper opening meets the requirements. This constitutes a constrained optimization problem. By minimizing the constructed objective function, the target damper and its corresponding target control increment are determined. Optionally, in the PLC (Programmable Logic Controller) of the sludge drying equipment, the objective function can be minimized using algorithms such as the effective set method or the interior point method to obtain the corresponding control sequence. This control sequence includes multiple damper control increments corresponding to different steps. The first damper control increment is used as the target control increment, and the corresponding target damper is determined for adjustment.

[0035] As can be seen from the above scheme, this scheme constructs a predictive model to predict future temperature changes in the system and correlates the opening degree of the air valve with temperature changes. This transforms the control problem of the air valve into an online quadratic programming solution, thereby performing constrained rolling optimization. By comprehensively considering the control effect and cost, the optimal control increment under the constraints is determined, and the air valve is adjusted. This ensures a uniform temperature distribution throughout the entire conveyor belt area during the drying process, effectively improving the sludge drying effect. This allows for uniform drying of the sludge, avoiding excessive and ineffective air valve operation and over-drying of already dried areas. It also enables on-demand air supply, which helps save energy.

[0036] Optionally, the objective function includes a first term corresponding to the tracking error and a second term corresponding to the control penalty. In one embodiment, the first term corresponding to the tracking error can be determined by calculating the difference between the predicted sludge temperature value and the target temperature value, and the second term corresponding to the control penalty can be determined by introducing the valve control increment. Specifically, in the process of constructing the objective function, a first error value between the predicted sludge temperature value and the target temperature value is determined, and the sum of squared errors is calculated based on the first error value according to a first weight; and the sum of squared increments is calculated based on the valve control increment according to a second weight.

[0037] Then, using the sum of squared errors as the first term of the corresponding tracking error and the sum of squared increments as the second term of the corresponding control penalty, the cumulative sum of the sum of squared errors and the sum of squared increments is determined as the objective function. This objective function represents the change in the first error value between the predicted sludge temperature value and the target temperature value under different valve control increments. The corresponding function expression is as follows:

[0038]

[0039]

[0040] Where, N p For the predicted time domain range, Nc Then the corresponding control step, To predict sludge temperature, Y ref For the target temperature, For the incremental control of the air valve, Q is the first weight and R is the second weight. This is the weighted sum of squared errors. This is the weighted sum of squared increments.

[0041] Understandably, the first weight represents the weight assigned to the temperature sensor error. For example, a higher weight can be assigned to the temperature sensor located at the feed end in the first weight to ensure that the temperature at the selected sensor location is close to the target temperature during optimization. The second weight is used to limit the increment of the damper opening to prevent frequent large-amplitude damper movements by configuring appropriate weights. To minimize this objective function, the damper can be adjusted to make the temperature closer to the target temperature while minimizing the increment of the damper opening. This allows for adjusting the damper control increment to determine the control increment between the two constraints. Therefore, by constructing an objective function to represent the constraints on damper control, this scheme can solve for the damper control increment by minimizing the objective function, thereby determining the optimal damper control increment and achieving a smooth and uniform control effect.

[0042] Figure 2 The schematic diagram illustrates the steps for determining the target control increment according to an embodiment of this application. In one embodiment, after constructing the corresponding objective function for the constrained optimization problem, the objective function is converted into the corresponding standard form based on the standard form of quadratic programming, and then the optimal solution under the effective set is determined through iterative loops. Specifically, the steps include the following: Step S210: Based on the Hessian matrix and gradient vector, the objective function is expressed in the standard form of quadratic programming to obtain the transformed objective function.

[0043] Step S220: Based on the function constraints, determine the opening constraint inequality corresponding to the opening limit range and the incremental constraint inequality corresponding to the maximum value of the opening change.

[0044] Step S230: Under the constraints of the opening constraint inequality and the increment constraint inequality, the effective set method is used to iterate and determine the valve control increment that satisfies the preset threshold of the Lagrange multiplier as the target control increment.

[0045] Step S240: Use the target control increment as the change in the opening of the air valve, and adjust the opening of the target air valve.

[0046] Understandably, the Hessian matrix is ​​a symmetric positive definite matrix of the second-order partial derivatives of the objective function, used to determine the quadratic term structure and solution efficiency. The gradient vector, on the other hand, is a vector of the first-order partial derivatives of the objective function, used to determine the linear terms. Combining the Hessian matrix and the gradient vector, the objective function is transformed into a form expressed in the standard quadratic programming paradigm. This standardizes the objective function and yields the transformed objective function, which is expressed as follows:

[0047]

[0048] Where X is the decision vector containing multiple steps corresponding to the valve control increments, H is the Hessian matrix, and f is the gradient vector. Functional constraints are also converted into linear inequalities. For example, if the set functional constraints include the opening limit range and the maximum value of the opening change, they are expressed in inequality form, thus obtaining the opening constraint inequality corresponding to the opening limit range and the increment constraint inequality corresponding to the maximum value of the opening change. For example, the set opening limit range is [U... min U max Accordingly, the corresponding inequality can be determined as U(k+τ)≤U max U(k+τ)≥U min .

[0049] Furthermore, in this embodiment, the transformed objective function can be minimized using the effective set method. The effective set method, an algorithm that utilizes effective constraints to construct subproblems and solve new iteration points, after determining the opening constraint inequality and the increment constraint inequality, sets an initial feasible point under the constraints to construct the corresponding effective set. The constructed initial effective set includes constraints where equality holds at the initial feasible point. The algorithm iterates continuously through loops, using the Lagrange multiplier method in each loop to determine the corresponding Lagrange multipliers. It also searches for the valve control increment whose Lagrange multipliers satisfy a preset threshold, using this as the target control increment. Therefore, this scheme, through the effective set method based on Lagrange multipliers, can determine the update of the effective set through multipliers. While ensuring convergence, it can effectively adapt to quadratic programming problems of different scales, enabling efficient determination of the current optimal control increment for valve control optimization, thereby adjusting the valve and improving the sludge drying effect of the sludge drying equipment.

[0050] Optionally, in the process of determining the target control increment using the effective set method, during the iterative loop, each iteration determines the effective set based on the opening constraint inequality and the increment constraint inequality. The effective set includes several constraint equations related to the valve control increment that satisfy equality conditions in the opening constraint inequality and the increment constraint inequality. Specifically, the generation process of the effective set is illustrated using a single variable combined with two constraint terms. For example, for a single valve, the valve control increment ΔU to be optimized corresponds to two constraint terms: constraint 1: ΔU≤5 (i.e., the constraint that the maximum opening in a single step is 5%), and constraint 2: ΔU≥5. 5 (meaning a single-step maximum constraint of 5%). The initial point is any feasible point that satisfies all constraints. Taking ΔU0=0 as an example, by checking whether it satisfies the above constraints, the inequality in constraint 1 is true, and the inequality in constraint 2 is also true. Therefore, the initial effective set W0= This is an empty set, meaning no constraint term is equal to ΔU0. Furthermore, taking the initial point selection ΔU0=5 as an example, by checking whether it satisfies the above constraints, the equality sign in constraint 1 is true, and the inequality sign in constraint 2 is true. Therefore, the initial valid set W0={constraint 1}, meaning only constraint 1 is equal to ΔU0. It should be noted that the valid set in a loop is determined in the same way.

[0051] Then, based on the effective set, a quadratic programming problem with equality constraints is performed on the objective function to determine the value of the corresponding search direction. Specifically, solving the quadratic programming problem with equality constraints on the current effective set can be done using the Lagrange multiplication method. That is, based on the constructed Lagrange function, the derivative of the transformed objective function and the constraint equations is taken to calculate the search direction. This search direction represents the direction from the control increment determined in the previous iteration to the control increment determined in the current iteration. The step size can be represented by the corresponding numerical value, as shown by the following formula:

[0052] in, For the search direction, The control increment determined in the previous cycle, The control increment is determined for this round of iteration. When the value corresponding to the search direction is not 0, the maximum step size is determined while satisfying the opening constraint inequality and the increment constraint inequality to update the effective set. This can be understood as follows: after determining the search direction, the effective set is further adjusted; that is, the maximum step size along the search direction that does not violate the function constraints is determined, and the effective set is re-determined based on the value corresponding to the search direction for the next round of iteration. When the value corresponding to the search direction is 0, the Lagrange multipliers corresponding to the effective set are determined. When all Lagrange multipliers are greater than or equal to 0, the valve control increment corresponding to the current effective set is determined as the target control increment. It is conceivable that if any Lagrange multiplier is less than 0, the constraint equality corresponding to that Lagrange multiplier is removed from the effective set.

[0053] In response, this solution utilizes the effective set method to explore the constraint boundary and gradually approach the global optimum, thereby transforming the optimization problem into solving a finite number of equality-constrained quadratic programming problems. This approach improves efficiency while maintaining accuracy, and is beneficial for ensuring the effectiveness and real-time performance of valve control in sludge drying applications.

[0054] Figure 3 This is a schematic diagram illustrating the steps for correcting a predicted sludge temperature value according to an embodiment of this application. In one embodiment, after determining the opening degree of the target air valve, the prediction error between the predicted sludge temperature value in the previous control cycle k and the measured value is calculated based on the actual measured value of the temperature sensor in the next control cycle k+1. The predicted temperature value in the next control cycle k+1 is then corrected based on this error. The specific steps include: Step S310: After adjusting the opening of the target air valve, determine the second error value between the predicted sludge temperature value and the measured value based on the temperature sensor measurement value.

[0055] Step S320: Correct the predicted sludge temperature value for the next control cycle according to the second error value, and adjust the valve opening increment for the next control cycle accordingly.

[0056] Understandably, after adjusting the target air valve according to the target control increment and maintaining the valve opening for the preset duration, i.e., during control cycle k+1, the corresponding temperature data can be re-detected by the temperature sensor and used as the measured value. Based on this, the prediction error can be determined by comparing the difference between the predicted sludge temperature value of the previous cycle and the measured value of the next cycle within two adjacent control cycles. That is, the difference between the predicted sludge temperature value of control cycle k and the measured value of control cycle k+1 is calculated and used as the second error value. For example, if the actual temperature reading in control cycle k+1 is 89.5℃ instead of the predicted 88℃, the second error value can be determined as e(k+1) = 89.5 - 88 = 1.5℃. This positive error indicates that our model may be somewhat pessimistic, or that the system responds faster than the model.

[0057] Then, the predicted sludge temperature within the control period k+1 is corrected according to the second error value. Specifically, after determining the second error value, this second error value e(k+1) is used to correct subsequent predictions, such as correcting the predicted value for the control period k+1 to... (k+τ|k+1)= (k+τ|k+1)+e(k+1) thus corrects the overall prediction curve upward by 1.5℃. Since the control action of the air valve opening will cause temperature changes, this correction means that the new round of optimization does not need to open the air valve so aggressively. It can make more accurate and gentler decisions on the air valve, realizing closed-loop feedback, so that the temperature prediction can track the actual system, and the updated air valve adjustment can be more accurate.

[0058] Figure 4 This is a schematic diagram of a valve control device according to an embodiment of this application. The device is used to execute the valve control method provided in the above embodiment, and it possesses the functional modules for executing the method and the beneficial effects. This valve control device is applied to a sludge drying equipment. The sludge drying equipment has several temperature sensors installed at the feed end, intermediate section, and discharge end, and also has multiple air valves. As shown in the figure, the valve control device includes an incremental prediction module 401, a temperature prediction module 402, a function configuration module 403, and an incremental determination module 404.

[0059] The incremental prediction module 401 is configured to predict the first temperature change value after a preset time by using a preset pulse response matrix, after acquiring the initial temperature value collected by all temperature sensors and the opening degree of all air valves in the current control cycle. The pulse response matrix is ​​used to record the temperature increment detected by all temperature sensors at a unit opening degree for each air valve within the preset time. The temperature prediction module 402 is configured to determine the predicted sludge temperature value after a preset time period based on the first temperature change value and the initial temperature, in the mapping relationship between the temperature value corresponding to the pre-set convolution sum function and the air valve control increment. The convolution sum function is used to determine the predicted sludge temperature value according to the first temperature change value, the initial temperature and the air valve control increment. The function configuration module 403 is configured to construct an objective function that is associated with the predicted sludge temperature value, the target temperature value, and the air valve control increment, based on the predicted sludge temperature value and the target temperature value. The incremental determination module 404 is configured to minimize the objective function under preset function constraints to obtain the target control increment of the corresponding target air valve, and adjust the opening of the target air valve according to the target control increment.

[0060] Based on the above embodiments, the impulse response matrix is ​​a three-dimensional matrix associated with the air valve, temperature sensor, and preset duration. The elements in the impulse response matrix represent temperature increments. The temperature increments are obtained by sequentially applying a step signal to each air valve to record the response data of all temperature sensors within the preset duration. The step signal is used to control the opening degree of the air valve.

[0061] Based on the above embodiments, the convolution function is used to output the sum of the cumulative product of the valve control increment and the first temperature change value and the zero input response value, and to use it as the predicted temperature value. The zero input response value is the sum of the initial temperature value and the second temperature change value detected by the sludge drying equipment while maintaining the valve opening at the same level for a preset time.

[0062] Based on the above embodiments, the function configuration module 403 is specifically configured as follows: Determine the first error value between the predicted sludge temperature value and the target temperature value, and calculate the sum of squared errors based on the first error value according to the first weight; The sum of squared increments is calculated by applying the second weight to the control increment of the damper. The cumulative sum of the sum of squared errors and the sum of squared increments is determined as the objective function, which represents the change in the first error value between the predicted sludge temperature value and the target temperature value under different valve control increments.

[0063] Based on the above embodiments, the function constraints include the opening limit range corresponding to the air valve and the maximum value of the opening change of the air valve in each control cycle. The incremental determination module 404 is specifically configured as follows: Based on the Hessian matrix and gradient vector, the objective function is expressed in the standard form of quadratic programming to obtain the transformed objective function; Based on the function constraints, determine the opening constraint inequality for the corresponding opening limit range and the incremental constraint inequality for the maximum value of the corresponding opening change. Under the constraints of the opening constraint inequality and the increment constraint inequality, the effective set method is used to iterate and determine the valve control increment that satisfies the preset threshold of the Lagrange multiplier as the target control increment; The target control increment is used as the change in the valve opening to adjust the opening of the target valve.

[0064] Based on the above embodiments, the incremental determination module 404 is further configured as follows: During the iterative process, based on the opening constraint inequality and the increment constraint inequality, the effective set is determined. The effective set includes several constraint inequalities related to the valve control increment that satisfy the equality condition in the opening constraint inequality and the increment constraint inequality. Based on the effective set, a quadratic programming problem with equality constraints is performed on the objective function to determine the value of the corresponding search direction; If the value of the corresponding search direction is not 0, determine the maximum step size that satisfies the opening constraint inequality and the increment constraint inequality, so as to update the effective set; When the value of the corresponding search direction is 0, determine the Lagrange multiplier corresponding to the effective set. When all Lagrange multipliers are greater than or equal to 0, determine the valve control increment corresponding to the current effective set as the target control increment.

[0065] Based on the above embodiments, the device further includes an error correction module, which is configured as follows: After adjusting the opening of the target air valve, a second error value between the predicted sludge temperature value and the measured value is determined based on the temperature sensor measurement. The predicted sludge temperature value for the next control cycle is corrected based on the second error value, and the valve opening increment for the next control cycle is adjusted accordingly.

[0066] It is worth noting that in the embodiments of the above-mentioned device, the modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each module are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0067] Figure 5This is a schematic diagram of a sludge drying device provided in an embodiment of this application. The device is used to execute the air valve control method provided in the above embodiment and has corresponding functional modules and beneficial effects for executing the method. As shown in the figure, the device includes a processor 501, a memory 502, an input device 503, and an output device 504. The number of processors 501 can be one or more; the figure shows one processor 501 as an example. The processor 501, memory 502, input device 503, and output device 504 can be connected via a bus or other means; the figure shows a connection via a bus as an example. The memory 502, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the air valve control method in the embodiments of this application. The processor 501 executes various corresponding functional applications and data processing by running the software programs, instructions, and modules stored in the memory 502, thereby realizing the above-mentioned air valve control method.

[0068] The memory 502 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data recorded or created during use. Furthermore, the memory 502 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 502 may further include memory remotely configured relative to the processor 501, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0069] The input device 503 can be used to input corresponding digital or character information to the processor 501, and to generate key signal inputs related to the user settings and function control of the device; the output device 504 can be used to send or display key signal outputs related to the user settings and function control of the device.

[0070] This application also provides a storage medium storing computer-executable instructions, which, when executed by a processor, are used to perform relevant operations in the damper control method provided in any embodiment of this application.

[0071] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0072] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0073] Note that the above description is merely a preferred embodiment and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the appended claims.

Claims

1. A method for controlling an air valve, characterized in that, The method is applied to sludge drying equipment, which is equipped with several temperature sensors at the feed end, intermediate section, and discharge end, and also includes multiple air valves. Given the initial temperature values ​​collected by all temperature sensors and the opening degree of all air valves within the current control cycle, the first temperature change value after a preset time is predicted by a preset pulse response matrix. The pulse response matrix is ​​used to record the temperature increment detected by all temperature sensors at a unit opening degree for each air valve within the preset time. Based on the first temperature change value and the initial temperature, the predicted sludge temperature value after a preset time is determined in the mapping relationship between the temperature value and the air valve control increment corresponding to the preset convolution sum function. The convolution sum function is used to determine the predicted sludge temperature value according to the first temperature change value, the initial temperature and the air valve control increment. Based on the predicted sludge temperature value and the target temperature value, an objective function is constructed that is associated with the predicted sludge temperature value, the target temperature value, and the valve control increment; Under preset function constraints, the objective function is minimized to obtain the target control increment of the corresponding target air valve, and the opening degree of the target air valve is adjusted according to the target control increment.

2. The air valve control method according to claim 1, characterized in that, The pulse response matrix is ​​a three-dimensional matrix associated with the air valve, the temperature sensor, and a preset duration. The elements in the pulse response matrix represent the temperature increment. The temperature increment is obtained by sequentially applying a step signal to each air valve to record the response data of all temperature sensors within the preset duration. The step signal is used to control the opening degree of the air valve.

3. The air valve control method according to claim 1, characterized in that, The convolution function is used to output the sum of the cumulative product of the control increment of the air valve and the first temperature change value and the zero input response value, which is used as the predicted temperature value. The zero input response value is the sum of the initial temperature value and the second temperature change value detected by the sludge drying equipment while maintaining the air valve opening at the same level for a preset time.

4. The air valve control method according to claim 1, characterized in that, The step of constructing an objective function relating the predicted sludge temperature value, the target temperature value, and the valve control increment based on the predicted sludge temperature value and the target temperature value includes: A first error value is determined between the predicted sludge temperature value and the target temperature value, and the sum of squared errors is calculated based on the first error value according to a first weight. The sum of squared increments is calculated by applying the second weight to the control increment of the air valve. The cumulative sum of the sum of squared errors and the sum of squared increments is determined as the objective function, which represents the change in the first error value between the predicted sludge temperature value and the target temperature value under different air valve control increments.

5. The air valve control method according to claim 1, characterized in that, The function constraints include the opening limit range corresponding to the damper and the maximum value of the damper opening change in each control cycle. The step of minimizing the objective function under the preset function constraints to obtain the target control increment of the corresponding target damper, and adjusting the opening of the target damper according to the target control increment, includes: Based on the Hessian matrix and gradient vector, the objective function is expressed in the standard form of quadratic programming to obtain the transformed objective function; Based on the aforementioned function constraints, determine the opening constraint inequality corresponding to the opening limit range and the incremental constraint inequality corresponding to the maximum value of the opening change. Under the constraints of the opening constraint inequality and the increment constraint inequality, the effective set method is used to iterate and determine the valve control increment that satisfies the preset threshold of the Lagrange multiplier as the target control increment; The target control increment is used as the change in the opening of the air valve to adjust the opening of the target air valve.

6. The air valve control method according to claim 5, characterized in that, The step of iteratively determining the valve control increment that satisfies a preset threshold using the effective set method includes: During the iterative loop, based on the opening constraint inequality and the incremental constraint inequality, an effective set is determined. The effective set includes several constraint equations related to the valve control increment that satisfy the equality condition in the opening constraint inequality and the incremental constraint inequality. Based on the effective set, the objective function is solved by a quadratic programming problem with equality constraints to determine the value of the corresponding search direction; If the value of the corresponding search direction is not 0, determine the maximum step size that satisfies the opening constraint inequality and the increment constraint inequality, so as to update the effective set; When the value of the corresponding search direction is 0, the Lagrange multiplier corresponding to the effective set is determined, so that when all Lagrange multipliers are greater than or equal to 0, the valve control increment corresponding to the current effective set is determined as the target control increment.

7. The method for controlling a damper according to any one of claims 1-6, characterized in that, Also includes: After adjusting the opening of the target air valve, a second error value between the predicted sludge temperature value and the measured value is determined based on the measured value of the temperature sensor. The predicted sludge temperature value for the next control cycle is corrected according to the second error value, and the valve opening increment for the next control cycle is adjusted accordingly.

8. A damper control device, characterized in that, This device is applied to sludge drying equipment. The sludge drying equipment is equipped with several temperature sensors at the feed end, intermediate section, and discharge end, and also includes multiple air valves. The device comprises: The incremental prediction module is configured to predict the first temperature change value after a preset time period by using a preset pulse response matrix, given the initial temperature values ​​collected by all temperature sensors and the opening degree of all air valves within the current control cycle. The pulse response matrix is ​​used to record the temperature increment detected by all temperature sensors at a unit opening degree for each air valve within the preset time period. The temperature prediction module is configured to determine the predicted sludge temperature value after a preset time period based on the first temperature change value and the initial temperature, in the mapping relationship between the temperature value and the air valve control increment corresponding to the preset convolution sum function. The convolution sum function is used to determine the predicted sludge temperature value according to the first temperature change value, the initial temperature and the air valve control increment. The function configuration module is configured to construct an objective function associated with the predicted sludge temperature value, the target temperature value, and the air valve control increment based on the predicted sludge temperature value and the target temperature value. The incremental determination module is configured to minimize the objective function under preset function constraints to obtain the target control increment of the corresponding target air valve, and adjust the opening of the target air valve according to the target control increment.

9. A sludge drying device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the damper control method as described in any one of claims 1-7.

10. A storage medium for storing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a processor, are used to perform the valve control method as described in any one of claims 1-7.

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